短时公交客流的弦不变量预测方法

doi:10.16451/j.cnki.issn1003-6059.201809007

摘要
图/表
参考文献(0)
相关文章 (15)

全文: PDF (760 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

短时公交客流预测是智能公交系统动态调度的基础.文中根据短时公交客流数据特性,提出基于弦理论的短时公交客流预测方法,模拟弦结构建立弦不变量客流预测模型(SI-PFPM),并采用遗传算法优化SI-PFPM中各参数.提出基于动态时间弯曲距离的仿射传播(AP)聚类算法,对短时公交客流时间序列进行聚类分析.利用SI-PFPM预测聚类子集数据,并分析预测残差,验证SI-PFPM可以预测短时公交客流的假设成立.最后将SI-PFPM的预测性能与现有方法进行对比分析,验证SI-PFPM对短时公交客流预测的有效性.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	董红召
	刘倩
	付凤杰

关键词 ：弦理论, 弦不变量, 短时公交客流预测, 遗传算法, 时间序列聚类

Abstract：

The prediction of short-term bus passenger flow is the basis of the dynamic scheduling of the intelligent bus system. Therefore, according to the data characteristics of short-term passenger flow of bus, a short-term bus passenger flow prediction method based on string theory is proposed. A string invariants passenger flow prediction model(SI-PFPM) is constructed by simulating the string structure and the genetic algorithm is adopted to optimize the parameters of SI-PFPM. An AP clustering algorithm (DTW-AP) based on dynamic time warping(DTW) distance is proposed to perform clustering analysis on the passenger flow time series of short-time buses. SI-PFPM is employed to predict the short-term passenger flow data, and the predicted residual error is analyzed. The result shows that SI-PFPM is effective for the prediction of short-term bus passenger flow. Finally, the prediction performance of SI-PFPM is compared with the existing methods, and the effectiveness of SI-PFPM in short-term bus passenger flow prediction is verified.

Key words： String Theory String Invariants Prediction of Short-Term Passenger Flow of Bus Genetic Algorithm Clustering of Temporal Sequences

收稿日期: 2017-12-19

ZTFLH:

U 492.4+13

基金资助:

国家自然科学基金项目(No.61773347)、浙江省自然科学基金项目(No.LY17F030017)、杭州市重大科技专项项目(No.20162013 A06)资助

作者简介: 董红召(通讯作者),博士,教授,主要研究方向为智能交通系统.E-mail:its@zjut.edu.cn.刘倩,硕士研究生,主要研究方向为智能交通系统.E-mail:862054822@qq.com.

引用本文:

董红召，刘倩，付凤杰. 短时公交客流的弦不变量预测方法[J]. 模式识别与人工智能, 2018, 31(9): 845-855. DONG Hongzhao, LIU Qian, FU Fengjie. Prediction Method of String Invariants of Short-Term Passenger Flow of Bus. , 2018, 31(9): 845-855.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201809007 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I9/845

[1] 《中国公路学报》编辑部.中国公路交通学术研究综述.2012.中国公路学报, 2012, 25(3): 2-50.
(Editorial Department of China Journal of Highway and Transport. An Academic Research Summary on China Highway and Transport: 2012. China Journal of Highway and Transport, 2012, 25(3): 2-50.)
[2] 滕靖,王何斐,杨新征,等.基于乘客主观感知的公交客流拥塞量化表征模型.中国公路学报, 2018, 31(6): 299-307.
(TENG J, WANG H F, YANG X Z, et al. Subjective Perception Based Passenger Congestion Quantification for Bus Operation. China Journal of Highway and Transport, 2018, 31(6): 299-307.)
[3] 高坤,涂辉招,李浩,等.出行者对不同交通方式行程时间可靠度和车内拥挤度的感知差异性.中国公路学报, 2017, 30(7): 126-133.
(GAO K, TU H Z, LI H, et al. Travelers′ Perception Differences in Travel Time Reliability and Crowding of Mode-Choice in Multimodal Networks. China Journal of Highway and Transport, 2017, 30(7): 126-133.)
[4] LUKE H, MARTIN K. The Distribution of Crowding Costs in Public Transport: New Evidence from Paris. Transportation Research Part A(Policy and Practice), 2015, 77: 182-201.
[5] 张春辉,宋瑞,孙杨.基于卡尔曼滤波的公交站点短时客流预测.交通运输系统工程与信息, 2011, 11(4): 154-159.
(ZHANG C H, SONG R, SUN Y. Kalman Filter-Based Short Term Passenger Flow Forecasting on Bus Stop. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(4): 154-159.)
[6] 刘凯,李文权,赵锦焕.短时公交客流小波预测方法研究.交通运输工程与信息学报, 2010, 8(2): 111-117.
(LIU K, LI W Q, ZHAO J H. Study on Wavelet Forecast Method for Short-Term Passenger Flow. Journal of Transportation Engineering and Information, 2010, 8(2): 111-117.)
[7] CHEN Q, LI W Q, ZHAO J H. The Use of LS-SVM for Short-Term Passenger Flow Prediction. Transport, 2011, 26(1): 5-10.
[8] 郭士永,李文权,白薇,等.基于最小二乘向量机的公交站点短时客流预测.武汉理工大学学报(交通科学与工程版), 2013, 37(3): 603-607.
(GUO S Y, LI W Q, BAI W, et al. Prediction of Short-Term Passenger Flow on a Bus Stop Based on LSSVM. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2013, 37(3): 603-607.)
[9] LIU L J, CHEN R C. A Novel Passenger Flow Prediction Model Using Deep Learning Methods. Transportation Research Part C(Emerging Technologies), 2017, 84: 74-91.
[10] 杨信丰,刘兰芬.基于AP聚类的支持向量机公交站点短时客流预测.武汉理工大学学报(交通科学与工程版), 2016, 40(1): 36-40.
(YANG X F, LIU L F. Short-Term Passenger Flow Forecasting on Bus Station Based on Affinity Propagation and Support Vector Machine. Journal of Wuhan University of Technology(Transportation Science ＆ Engineering), 2016, 40(1): 36-40.)
[11] PINCAK R. The String Prediction Models as Invariants of Time Series in the Forex Market. Physica A(Statistical Mechanics And ITS Applications), 2013, 392(24): 6414-6426.
[12] ZWIEBACH B. A First Course in String Theory. Cambridge, USA: Cambridge University Press, 2009.
[13] HORV TH D, PINCAK R. From the Currency Rate Quotations onto Strings and Brane World Scenarios. Physica A(Statistical Mechanics and ITS Applications), 2012, 391(21): 5172-5188.
[14] PINC‘K R, BARTOS‘E. With String Model to Time Series Forecasting. Physica A(Statistical Mechanics and ITS Applications), 2015, 436: 135-146.
[15] DEB K, PRATAP A, AGARWAL S, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation,2002, 6(2): 182-197.
[16] GABRIEL L Q, ENRIQUE A. Parallel Genetic Algorithms: Theory and Real World Applications. Berlin, Germany: Springer, 2011.
[17] 贺锋,秦晓丽,方勇纯. 一种基于遗传算法的移动机器人自定位方法.模式识别与人工智能, 2009, 22(1): 142 -147.
(HE F, QIN X L, FANG Y C. A Genetic Algorithm Based Auto-nomous Localization Strategy for Mobile Robots. Pattern Recognition and Artificial Intelligence, 2009, 22(1): 142-147.)
[18] 陈希孺.概率论与数理统计.合肥:中国科学技术大学出版社, 2009.
(CHEN X R. Probability Theory and Mathematics Statistics. Hefei, China: Press of University of Science and Technology of China, 2009.)
[19] 李航.统计学习方法.北京:清华大学出版社, 2012.
(LI H. Statistical Learning Method. Beijing, China: Tsinghua University Press, 2012.)
[20] 吴景龙,杨淑霞,刘承水.基于遗传算法优化参数的支持向量机短期负荷预测方法.中南大学学报(自然科学版), 2009, 40(1): 180-184.
(WU J L, YANG S X, LIU C S. Parameter Selection for Support Vector Machines Based on Genetic Algorithms to Short-Term Power Load Forecasting. Journal of Central South University(Science and Technology), 2009, 40(1): 180-184.)
[21] SUN Y X, LENG B. GUAN W. A Novel Wavelet-SVM Short-Time Passenger Flow Prediction in Beijing Subway System. Neurocomputing, 2015, 166: 109-121.
[22] XIAO Z, YE S J, ZHONG B, et al. BP Neural Network with Rough Set for Short Term Load Forecasting. Expert Systems with Applications, 2009, 36(1): 273-279.